Finance - Accounting Seminars

 

 

Truth-telling is essential to efficient allocation of resources but is often undermined by strategic misreporting. We show how verifiably fair randomness, complemented by other blockchain functionalities, can lead to truth-telling at the cost of introduction of random noise in information transmission. The optimal trade-off between truthfulness and precision yields equilibria that outperform those attainable without randomization in information transmission. We characterize two classes of information equilibria: information designated to directly trigger value transfer via decentralized consensus (“enforcive information”), and strategic information sharing between Bayesian-rational decision makers (“advisive information”). Our findings demonstrate usefulness of randomized transmission in broad financial settings and illustrate the benefits of blockchain technology for optimal mechanism design.

Using a simple model of firm borrowing with standard ingredients, we show that commonly used empirical approaches in the literature do not recover the impact of credit supply shocks on loan-level lending, on total firm-level borrowing or on real outcomes. We propose new estimators that recover these effects. We apply our methodology to the 2011 credit crisis in Spain and show that it implies significantly smaller effects of loan supply shocks than those generated by current empirical approaches.

Abadie et al. (QJE 2023) propose two new variance estimators, one analytical and one bootstrap, for the important use case of a binary treatment where the number of clusters is finite (e.g., U.S. states), there are many treated and control units in each cluster, and only some clusters are observed. They study a setting where treatment is randomly assigned within cluster but the treatment effect and probability of assignment to treatment vary across clusters. Their simulations show that when all clusters are observed, conventional clustered standard errors (c.s.e.’s) can be severely conservative, but they find correct s.e.’s and coverage for their proposed two-stage cluster bootstrap (TSCB), with both OLS and cluster fixed effects, and good performance for an analytical variance estimator with cluster fixed effects. We confirm that c.s.e.’s are conservative when all clusters are observed, but show that their results are a corner case. When less than all clusters are observed (even slightly less), the TSCB produces neither correct s.e.’s nor correct coverage and can be inferior to c.s.e.’s or the wild cluster bootstrap. Their analytical variance estimator is also unreliable when only some clusters are observed. Which variance estimator comes closer to correct coverage depends on the sample, the treatment, the outcome, and the number of observed clusters. Moreover, a conservative s.e. does not necessarily imply conservative coverage. Including versus omitting even a single influential cluster (California, in their dataset) can greatly affect s.e. and coverage estimates

This article examines the effects of regulatory efforts aimed at enhancing minority shareholder power through voting mechanisms—particularly in markets characterized by concentrated ownership and by the use of majority-of-the-minority rules and remote (online) voting. While these mechanisms were intended to enhance public participation as a means to improve accountability and to subject potentially conflicted decisions—between managers, controlling shareholders, and the minority—to broader scrutiny, their eventual effect appears to have been the reinforcement of institutional investor influence. Retail investors, by contrast, appear to serve more as a defensive presence than an assertive force in corporate governance. Although retail shareholders are envisioned as a balancing force in corporate governance, their participation emerges as limited and inconsistent. Notably, retail participation tends to decline when institutional investors are involved. Thus, rather than improving corporate governance and reducing conflicts of interest, voting mechanisms such as majority-of-the-minority rules may in fact diminish the influence of retail investors in the presence of institutional participation, thereby heightening the potential for conflicts and power imbalances. Drawing on a unique dataset of shareholder votes requiring special approval procedures and enhanced disclosure, this study documents persistently low levels of retail engagement. Empirical analysis reveals that attendance at shareholder assemblies does not increase in response to the importance of proposals. Instead, participation appears to be driven by the number of items on the agenda, pointing to a focus on volume rather than substance. Retail voting patterns largely mirror those of institutional investors and show little intensification in more contested or consequential decisions. Overall, findings indicate that retail investors play a relatively limited role in shaping voting outcomes compared to institutional investors across the full set of assemblies. However, their influence tends to be more pronounced when voting against proposals. Notably, when both retail and institutional investors participate, the influence of retail support on the approval of proposals appears very limited and even arbitrary in comparison to that of institutional investors. These results highlight the unintended consequences of well-intentioned regulatory designs and call for a reassessment of how minority shareholder protections are operationalized in practice.

This paper constructs a sentiment measure for published central bank (CB) communications using natural language processing and FinBERT techniques (machine learning models adapted to analyze financial news). We analyze sentiment of various Fed and ECB communications but focus on FOMC minutes and ECB press conferences for our main results. We find that Fed and ECB sentiment move together. Using local projection analysis, we estimate impulse response functions (IRF) to central bank sentiment. We find that CB sentiment leads policy interest rates and the Taylor rule several quarters into the future. Fed sentiment lead the Fed funds rate also after controlling for Michigan sentiment. In turn, stock market returns and deflation expectations tend to lead Central Bank sentiment.

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Transaction Fee Mechanisms Robust to Welfare-Increasing Collusion

Barriers to Collusion-resistant Transaction Fee Mechanisms

 

 

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